1. Field of the Invention
The invention relates to a method for optimizing the automatic fluorescence pattern recognition in immunodiagnosis. It serves for optimizing reliability and accuracy in computer-aided automatic interpretation of fluorescence patterns in immunodiagnosis.
2. Discussion of the Prior Art
In the indirect immunofluorescence test (IIFT), the results are generally evaluated visually per field by assessing the fluorescence pattern or the image under a microscope. Alternatively, or in support of this, automated methods can be employed for pattern classification.
In medical immunodiagnosis, the detection of antibodies in a patient's serum is an indication of a specific clinical picture. Cells and tissue sections serve here as antigen substrates. The sample material from the patient (e.g. serum diluted with aqueous potassium salt solution) is incubated for testing with these antigen substrates. The antibodies that are to be detected, if present in the serum, bind to the solid-phase-bound antigens. Bound antibodies are made visible under the microscope by a fluorescence dye. Depending on the particular antibody, characteristic patterns become visible.
Antinuclear antibodies (ANA) are often tested on human epithelial cells (HEP), primate livers and other tissues, for example in patients with various rheumatic diseases. Fifty different antinuclear antibodies can be identified in this way. For example, antibodies against nDNS (lupus erythematosus), SLE, against centromeres (forms of progressive systemic sclerosis) and against nuclear dots (liver cirrhosis). In addition, the cytoplasm can also fluoresce, for example if antimitochondrial antibodies are present (diagnosis: primary biliary cirrhosis (PBC)).
It is equally possible for bacteria or virus-infected cells to be used as substrate and for the corresponding infectious diseases to be diagnosed in this way. The diagnosis with infected cells or viruses represents a highly valid diagnosis.
The immunofluorescence technique has many advantages. For example, many parameters are investigated, and many different antibodies are often identified with one substrate. The antigens are directly available, whereas in biochemical methods they first have to be isolated and bound. For this reason, the antigens are present in the best possible native form, and the antibody diagnosis with the immunofluorescence technique is in many cases particularly competent in diagnostic terms. Because of differences in the fluorescence patterns, it is also possible for specific and non-specific reactions to be differentiated particularly clearly from one another. By contrast, biochemical methods only indicate that a reaction has taken place, without the possibility of assessing its relevance, since specific reactions cannot be distinguished from non-specific reactions, something that is often done at a stroke in immunofluorescence by viewing in a microscope.
However, the fluorescence technique cannot be used willingly everywhere as a mass testing method. The evaluation of the fluorescence images is usually done visually and requires highly trained personnel, it is time-consuming, and one of its main weaknesses lies in the subjectivity of the assessment. Because of these disadvantages, work has for some time been carried out on automating the pattern recognition in order to make this process more efficient, more objective and more reliable.
The automatic computer-aided evaluation of fluorescence images takes place in several steps. These generally involve imaging, image processing, feature extraction and classification. The classification of the fluorescence images is done by allocating defined image features to classic features of fluorescence patterns. The occasional presence of several antibodies within the patient sample that is to be examined leads to a large number of possible mixed patterns. In the classification, however, the basic patterns from which the mixed forms are composed should be defined in order to determine all identifiable antibodies.
A generally recognized automated analysis of fluorescence images, as is described in the patent DE19801400 for example, has not as yet become widely accepted. This is due in particular to the fact that the quality of the automatic classification, characterized by the statistical parameters of sensitivity, specificity, relevance, segregation, correct classification rate and false classification rate, does not allow the automatic classification to form a basis for a medical diagnosis. The weaknesses of the existing automatic methods lie in two sets of circumstances:    1. On the one hand, the classification of the fluorescence patterns is based on a preceding feature extraction without the application of a permanently effective method for identification of relevant structures in the fluorescence image.    2. On the other hand, the evaluation of the extracted image features is carried out in the form of a hierarchical decision tree. The path to the end result is dependent on each branch directed away from the individual decision that is made. If one of these individual decisions is made incorrectly, this leads to an end result that is generally false.